Generative Design of Stable Semiconductor Materials Using Deep Learning And DFT

Semiconductor device technology has exceptionally developed in complexity since discovering the bipolar transistor. With the rapid advancement of various technologies, semiconductors with distinct properties are essential. Recently, deep-learning, data-mining, and density functional theory (DFT)- based high-throughput calculations were widely performed to discover potential semiconductors for diverse applications. CubicGAN is a generative adversarial network where high-throughput analyses were done to uncover mechanically and dynamically stable materials with the assistance of DFT. In our work, we screened the semiconductors using a binary classifier from materials found from the CubicGAN. Next, we performed DFT computations to study their thermodynamic stability based on energy-above-hull and formation energy. According to our studies, 12 stable semiconductors were found with a particular class of materials, which we label as AA′MH6. Those are BaNaRhH6, BaSrZnH6, BaCsAlH6, SrTlIrH6, KNaNiH6, NaYRuH6, CsKSiH6, CaScMnH6, YZnMnH6, NaZrMnH6, AgZrMnH6, AgZrMnH6, and ScZnMnH6. It could be shown that AA′MH6 with M=Mn and NaYRuH6 semiconductors have considerably different structural, mechanical, and thermodynamic properties compared to the rest of the AA′MH6 semiconductors. In this study, The maximum bandgap found was approximately 3.3 eV from KNaNiH6, while the minimum bandgap was about 1.3 eV from CaScMnH6. BaNaRhH6, BaCsAlH6, CsKSiH6, KNaNiH6, and NaYRuH6 were identified as wide-bandgap semiconductors, where bandgaps are greater than 2 eV. Furthermore, BaSrZnH6 and KNaNiH6 are a direct bandgap semiconductors, whereas other AA′MH6 semiconductors exhibit indirect bandgaps.